They also depend on the design space to somewhat friendly in nature and can be modelled by a surrogate, so that exploit/explore can be modelled in an acquisition function.

Also successive halving e.g. build on assumptions how the learning curve develops.

Bottom line is that there is hyperparams for hyperparam searches again. So one starts building hyperparam heuristics on top of the hyperparam search.

In the end there is no free lunch. But if hyperparam search strategy somewhat works in a domain it is a great tool. Good thing is that one can typically encode the design space in Blackbox optimization algorithms more easily.